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» Multiscale Conditional Random Fields for Image Labeling
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CVPR
2008
IEEE
14 years 10 months ago
Learning for stereo vision using the structured support vector machine
We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be ...
Yunpeng Li, Daniel P. Huttenlocher
PAMI
2008
198views more  PAMI 2008»
13 years 8 months ago
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation....
Richard Szeliski, Ramin Zabih, Daniel Scharstein, ...
AI
2011
Springer
13 years 8 days ago
Exploiting Conversational Features to Detect High-Quality Blog Comments
Abstract. In this work, we present a method for classifying the quality of blog comments using Linear-Chain Conditional Random Fields (CRFs). This approach is found to yield high a...
Nicholas FitzGerald, Giuseppe Carenini, Gabriel Mu...
ICIP
2001
IEEE
14 years 10 months ago
Coding theoretic approach to image segmentation
This paper introduces multi-scale tree-based approaches to image segmentation, using Rissanen's coding theoretic minimum description length (MDL) principle to penalize overly...
Mário A. T. Figueiredo, Robert D. Nowak, Un...
CVPR
2010
IEEE
14 years 2 months ago
YouTubeCat: Learning to Categorize Wild Web Videos
Automatic categorization of videos in a Web-scale unconstrained collection such as YouTube is a challenging task. A key issue is how to build an effective training set in the pres...
Zheshen Wang, Ming Zhao, Yang Song, Sanjiv Kumar, ...